scholarly journals The Observation of Ligand-Binding-Relevant Open States of Fatty Acid Binding Protein by Molecular Dynamics Simulations and a Markov State Model

2019 ◽  
Vol 20 (14) ◽  
pp. 3476 ◽  
Author(s):  
Yue Guo ◽  
Mojie Duan ◽  
Minghui Yang
Keyword(s):  
Fatty Acid ◽  
Ligand Binding ◽  
Md Simulations ◽  
Binding Mechanism ◽  
Fatty Acid Binding ◽  
Markov State Model ◽  
Markov State ◽  
Intermediate States ◽  
Partially Unfolded ◽  
Open States

As a member of the fatty acids transporter family, the heart fatty acid binding proteins (HFABPs) are responsible for many important biological activities. The binding mechanism of fatty acid with FABP is critical to the understanding of FABP functions. The uncovering of binding-relevant intermediate states and interactions would greatly increase our knowledge of the binding process. In this work, all-atom molecular dynamics (MD) simulations were performed to characterize the structural properties of nativelike intermediate states. Based on multiple 6 μs MD simulations and Markov state model (MSM) analysis, several “open” intermediate states were observed. The transition rates between these states and the native closed state are in good agreement with the experimental measurements, which indicates that these intermediate states are binding relevant. As a common property in the open states, the partially unfolded α2 helix generates a larger portal and provides the driving force to facilitate ligand binding. On the other side, there are two kinds of open states for the ligand-binding HFABP: one has the partially unfolded α2 helix, and the other has the looser β-barrel with disjointing βD-βE strands. Our results provide atomic-level descriptions of the binding-relevant intermediate states and could improve our understanding of the binding mechanism.

scholarly journals Exploration of the Misfolding Mechanism of Transthyretin Monomer: Insights from Hybrid-Resolution Simulations and Markov State Model Analysis

Biomolecules ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 889
Author(s):  
Shuangyan Zhou ◽  
Jie Cheng ◽  
Ting Yang ◽  
Mingyue Ma ◽  
Wenying Zhang ◽  
...  
Keyword(s):  
Conformational Transition ◽  
Md Simulations ◽  
Outer Edge ◽  
State Model ◽  
Markov State Model ◽  
Markov State ◽  
Attr Amyloidosis ◽  
Secondary Structure Analysis ◽  
Partially Unfolded ◽  
Β Sheet

Misfolding and aggregation of transthyretin (TTR) is widely known to be responsible for a progressive systemic disorder called amyloid transthyretin (ATTR) amyloidosis. Studies suggest that TTR aggregation is initiated by a rate-limiting dissociation of the homo-tetramer into its monomers, which can rapidly misfold and self-assemble into amyloid fibril. Thus, exploring conformational change involved in TTR monomer misfolding is of vital importance for understanding the pathogenesis of ATTR amyloidosis. In this work, microsecond timescale hybrid-resolution molecular dynamics (MD) simulations combined with Markov state model (MSM) analysis were performed to investigate the misfolding mechanism of the TTR monomer. The results indicate that a macrostate with partially unfolded conformations may serve as the misfolded state of the TTR monomer. This misfolded state was extremely stable with a very large equilibrium probability of about 85.28%. With secondary structure analysis, we found the DAGH sheet in this state to be significantly destroyed. The CBEF sheet was relatively stable and sheet structure was maintained. However, the F-strand in this sheet was likely to move away from E-strand and reform a new β-sheet with the H-strand. This observation is consistent with experimental finding that F and H strands in the outer edge drive the misfolding of TTR. Finally, transition pathways from a near native state to this misfolded macrostate showed that the conformational transition can occur either through a native-like β-sheet intermediates or through partially unfolded intermediates, while the later appears to be the main pathway. As a whole, we identified a potential misfolded state of the TTR monomer and elucidated the misfolding pathway for its conformational transition. This work can provide a valuable theoretical basis for understanding of TTR aggregation and the pathogenesis of ATTR amyloidosis at the atomic level.

To probe the binding pathway of a selective compound (D089-0563) to c-MYC Pu24 G-quadruplex using free ligand binding simulations and Markov state model analysis

2020 ◽  
Vol 22 (39) ◽  
pp. 22567-22583
Author(s):  
Brian Chen ◽  
Griffin Fountain ◽  
Holli-Joi Sullivan ◽  
Nicholas Paradis ◽  
Chun Wu
Keyword(s):  
Ligand Binding ◽  
Promoter Region ◽  
Free Ligand ◽  
State Model ◽  
Binding Mechanism ◽  
Myc Oncogene ◽  
Markov State Model ◽  
Markov State ◽  
Anti Cancer ◽  
G Quadruplex

D089-0563 is a highly promising anti-cancer compound that selectively binds the transcription-silencing G-quadruplex element (Pu27) at the promoter region of the human c-MYC oncogene; however, its binding mechanism remains elusive.

scholarly journals Effect on ligand binding of arginine mutations in recombinant rat liver fatty acid-binding protein

1994 ◽  
Vol 297 (1) ◽  
pp. 103-107 ◽  
Author(s):  
A E Thumser ◽  
C Evans ◽  
A F Worrall ◽  
D C Wilton
Keyword(s):  
Fatty Acid ◽  
Ligand Binding ◽  
Rat Liver ◽  
Fatty Acid Binding Protein ◽  
Binding Protein ◽  
Liver Fatty Acid ◽  
Fatty Acid Binding ◽  
Acid Binding Protein ◽  
Wide Range ◽  
Arginine Residues

Rat liver fatty acid-binding protein is able to accommodate a wide range of non-polar anions in addition to long-chain fatty acids. The two arginine residues of rat liver fatty acid-binding protein, Arg122 and Arg126, have been mutated and the effect of mutation on ligand binding investigated. No significant decrease in affinity for the fluorescent fatty acid analogue, 11-(5-dimethylaminonaphthalenesulphonyl amino)undecanoic acid, or oleate was observed. However, the apparent affinity for oleoyl-CoA was slightly increased with the mutations Ala122 and Gln122 such that oleoyl-CoA rather than oleate became the preferred ligand for these mutants. Small changes in protein stability were observed with the Arg122 mutations. The lack of notable ionic involvement of the conserved internal residue Arg122 in ligand binding is consistent with the hypothesis that the mode of ligand binding in liver fatty acid-binding protein is markedly different from that of other members of this lipid-binding protein family.

scholarly journals PickPocket : Pocket binding prediction for specific ligands family using neural networks.

2020 ◽  
Author(s):  
Benjamin Thomas VIART ◽  
Claudio Lorenzi ◽  
María Moriel-Carretero ◽  
Sofia Kossida
Keyword(s):  
Neural Networks ◽  
Fatty Acid ◽  
Ligand Binding ◽  
Binding Sites ◽  
Structural Data ◽  
Binding Pocket ◽  
Fatty Acid Binding Proteins ◽  
Binding Prediction ◽  
Fatty Acid Binding ◽  
Binding Pockets

Most of the protein biological functions occur through contacts with other proteins or ligands. The residues that constitute the contact surface of a ligand-binding pocket are usually located far away within its sequence. Therefore, the identification of such motifs is more challenging than the linear protein domains. To discover new binding sites, we developed a tool called PickPocket that focuses on a small set of user-defined ligands and uses neural networks to train a ligand-binding prediction model. We tested PickPocket on fatty acid-like ligands due to their structural similarities and their under-representation in the ligand-pocket binding literature. Our results show that for fatty acid-like molecules, pocket descriptors and secondary structures are enough to obtain predictions with accuracy >90% using a dataset of 1740 manually curated ligand-binding pockets. The trained model could also successfully predict the ligand-binding pockets using unseen structural data of two recently reported fatty acid-binding proteins. We think that the PickPocket tool can help to discover new protein functions by investigating the binding sites of specific ligand families. The source code and all datasets contained in this work are freely available at https://github.com/benjaminviart/PickPocket .

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